In particular, using stochastic gradients in MAML update steps is crucial for RL problems since computation of exact gradients requires access to a large number of possible trajectories.

This study presents a comprehensive methodology for modeling and forecasting the historical time series of active fire spots detected by the AQUA\_M-T satellite in the Amazon, Brazil. The approach employs a mixed Recurrent Neural Network (RNN) model, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to predict the monthly accumulations of daily detected active fire spots. Data analysis revealed a consistent seasonality over time, with annual maximum and minimum values tending to repeat at the same periods each year. The primary objective is to verify whether the forecasts capture this inherent seasonality through machine learning techniques. The methodology involved careful data preparation, model configuration, and training using cross-validation with two seeds, ensuring that the data generalizes well to both the test and validation sets for both seeds. The results indicate that the combined LSTM and GRU model delivers excellent forecasting performance, demonstrating its effectiveness in capturing complex temporal patterns and modeling the observed time series. This research significantly contributes to the application of deep learning techniques in environmental monitoring, specifically in forecasting active fire spots. The proposed approach highlights the potential for adaptation to other time series forecasting challenges, opening new opportunities for research and development in machine learning and prediction of natural phenomena. Keywords: Time Series Forecasting; Recurrent Neural Networks; Deep Learning.

Influencer who used AI to dupe internet into thinking she caught her boyfriend cheating reveals she was inspired to do the skit because of real artificial voice scams. An influencer who made up a prank video claiming she used AI to catch her boyfriend cheating told Fox News her skit was a farce, but the voice-cloning technology she used to power the trick was not, and the skit was inspired by real scams. Mia Dio, a social media influencer with over 5 million TikTok followers, filmed a video of her using artificial intelligence to clone her boyfriend Billy's voice to see if he had cheated on her. The inspiration for the viral video came from reports of AI voice-cloning scams, she told Fox News. Dio used voicemails left by her boyfriend Billy to recreate his voice using AI software.

Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs. A series of methods represented by the adversarial training and its variants have proven as one of the most effective techniques in enhancing the DNN robustness. Generally, adversarial training focuses on enriching the training data by involving perturbed data. Despite of the efficiency in defending specific attacks, adversarial training is benefited from the data augmentation, which does not contribute to the robustness of DNN itself and usually suffers from accuracy drop on clean data as well as inefficiency in unknown attacks. Towards the robustness of DNN itself, we propose a novel defense that aims at augmenting the model in order to learn features adaptive to diverse inputs, including adversarial examples. Specifically, we introduce multiple paths to augment the network, and impose orthogonality constraints on these paths. In addition, a margin-maximization loss is designed to further boost DIversity via Orthogonality (DIO). Extensive empirical results on various data sets, architectures, and attacks demonstrate the adversarial robustness of the proposed DIO.

Spatio-temporal Human-Object Interaction (ST-HOI) detection aims at detecting HOIs from videos, which is crucial for activity understanding. In daily HOIs, humans often interact with a variety of objects, e.g., holding and touching dozens of household items in cleaning. However, existing whole body-object interaction video benchmarks usually provide limited object classes. Here, we introduce a new benchmark based on AVA: Discovering Interacted Objects (DIO) including 51 interactions and 1,000+ objects. Accordingly, an ST-HOI learning task is proposed expecting vision systems to track human actors, detect interactions and simultaneously discover interacted objects. Even though today's detectors/trackers excel in object detection/tracking tasks, they perform unsatisfied to localize diverse/unseen objects in DIO. This profoundly reveals the limitation of current vision systems and poses a great challenge. Thus, how to leverage spatio-temporal cues to address object discovery is explored, and a Hierarchical Probe Network (HPN) is devised to discover interacted objects utilizing hierarchical spatio-temporal human/context cues. In extensive experiments, HPN demonstrates impressive performance. Data and code are available at https://github.com/DirtyHarryLYL/HAKE-AVA.